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The representation of protein complexes in the Protein Ontology (PRO).

BMC bioinformatics | 2011

Representing species-specific proteins and protein complexes in ontologies that are both human- and machine-readable facilitates the retrieval, analysis, and interpretation of genome-scale data sets. Although existing protin-centric informatics resources provide the biomedical research community with well-curated compendia of protein sequence and structure, these resources lack formal ontological representations of the relationships among the proteins themselves. The Protein Ontology (PRO) Consortium is filling this informatics resource gap by developing ontological representations and relationships among proteins and their variants and modified forms. Because proteins are often functional only as members of stable protein complexes, the PRO Consortium, in collaboration with existing protein and pathway databases, has launched a new initiative to implement logical and consistent representation of protein complexes.

Pubmed ID: 21929785 RIS Download

Research resources used in this publication

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Associated grants

  • Agency: NHGRI NIH HHS, United States
    Id: U54 HG004028
  • Agency: NHGRI NIH HHS, United States
    Id: HG003622
  • Agency: NIGMS NIH HHS, United States
    Id: R01GM080646-03S1
  • Agency: NIGMS NIH HHS, United States
    Id: R01GM080646-04S2
  • Agency: NHGRI NIH HHS, United States
    Id: U41 HG003751
  • Agency: NIGMS NIH HHS, United States
    Id: R01 GM080646
  • Agency: NHGRI NIH HHS, United States
    Id: P41 HG003751
  • Agency: NIGMS NIH HHS, United States
    Id: R01GM080646

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This is a list of tools and resources that we have found mentioned in this publication.


EcoCyc (tool)

RRID:SCR_002433

Database for the bacterium Escherichia coli K-12 MG1655, the EcoCyc project performs literature-based curation of the entire genome, and of transcriptional regulation, transporters, and metabolic pathways. The long-term goal of the project is to describe the molecular catalog of the E. coli cell, as well as the functions of each of its molecular parts, to facilitate a system-level understanding of E. coli. EcoCyc is an electronic reference source for E. coli biologists, and for biologists who work with related microorganisms.

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Gene Ontology (tool)

RRID:SCR_002811

Computable knowledge regarding functions of genes and gene products. GO resources include biomedical ontologies that cover molecular domains of all life forms as well as extensive compilations of gene product annotations to these ontologies that provide largely species-neutral, comprehensive statements about what gene products do. Used to standardize representation of gene and gene product attributes across species and databases.

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Reactome (tool)

RRID:SCR_003485

Collection of pathways and pathway annotations. The core unit of the Reactome data model is the reaction. Entities (nucleic acids, proteins, complexes and small molecules) participating in reactions form a network of biological interactions and are grouped into pathways (signaling, innate and acquired immune function, transcriptional regulation, translation, apoptosis and classical intermediary metabolism) . Provides website to navigate pathway knowledge and a suite of data analysis tools to support the pathway-based analysis of complex experimental and computational data sets.

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RRID:SCR_005668

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Mouse Genome Database (tool)

RRID:SCR_012953

Community model organism database for laboratory mouse and authoritative source for phenotype and functional annotations of mouse genes. MGD includes complete catalog of mouse genes and genome features with integrated access to genetic, genomic and phenotypic information, all serving to further the use of the mouse as a model system for studying human biology and disease. MGD is a major component of the Mouse Genome Informatics.Contains standardized descriptions of mouse phenotypes, associations between mouse models and human genetic diseases, extensive integration of DNA and protein sequence data, normalized representation of genome and genome variant information. Data are obtained and integrated via manual curation of the biomedical literature, direct contributions from individual investigators and downloads from major informatics resource centers. MGD collaborates with the bioinformatics community on the development and use of biomedical ontologies such as the Gene Ontology (GO) and the Mammalian Phenotype (MP) Ontology.

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